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Record W2885186928 · doi:10.5539/elt.v11n9p26

Indisciplining the Curriculum From a Complex Perspective to Teach English

2018· article· en· W2885186928 on OpenAlexvenueno aff
Martha Moreno, Milton Pajaro

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLiteracy and Educational Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumProspectusCurriculum theoryCurriculum mappingPedagogyCriticismConstructive criticismMathematics educationNational curriculumEmergent curriculumSociologyPresentation (obstetrics)Foreign languageCurriculum developmentBilingual educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

This article presents a constructive criticism and also a prospectus about the suggested curriculum and the process of indisciplining the curriculum. It considers that through a process of indisciplining the English curriculum it would be possible to achieve the goals stated by the National Ministry of Education in terms of bilingualism and education quality. The article starts with a presentation of the educational reality of the country, the advances and transformation obtained in terms of bilingualism during the last years. Afterwards, it presents synthesis of the positive aspects of the suggested curriculum presented by the National Ministry of Education for the teaching of English as a foreign language in all the public schools of Colombia. Finally, the concept of indisciplining the curriculum in the English subject is presented as a proposal that would achieve meaningful progress in terms of English teaching and the education of students with critical thinking, complex and transformer of his/her society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.380
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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